Spatially, the region exhibits pronounced internal contrasts. Central districts such as Al-Sayeda Zainab, Bulaq, Azbakeya, and Shubra are characterized by compact urban morphology, high population densities, strong transit accessibility, and mixed land-use structures. Conversely, peripheral areas including 15th May City, Tura, and Atfih display dispersed development patterns, limited infrastructure provision, and inadequate coverage of formal public transport services. These disparities are further compounded by rising private car ownership, which earlier scenarios projected to exceed 2.5 million vehicles in the metropolitan area12<\/a>,14<\/a>, contributing to growing congestion, lengthy travel times, and unequal accessibility.<\/p>\n Although Greater Cairo is served by a relatively extensive metro network, persistent gaps in transit coverage, uneven population distribution, and fragmented urban governance continue to impede the effective implementation of Transit-Oriented Development (TOD) principles. These conditions highlight the necessity of adopting spatially explicit, localized TOD assessment frameworks capable of capturing the complexity of mobility and land-use interactions across the metropolitan landscape. Figure\u00a01<\/a> illustrates the administrative boundaries of the Greater Cairo Region, along with the key urban centers that form focal points of its continued growth and expansion. To capture the spatial heterogeneity of the region, the analysis in this study is conducted at the level of the\u00a0shiyakha, which serves as the fundamental spatial unit. In the Egyptian administrative hierarchy, a\u00a0shiyakha\u00a0is the smallest local urban district, functionally equivalent to a neighborhood or a census tract. Using the\u00a0shiyakha\u00a0is highly suitable for TOD assessment because it provides a fine-grained scale necessary to capture localized demographic, economic, and land-use variations that larger district-level averages might obscure.\u00a0Furthermore, the\u00a0shiyakha\u00a0is the most detailed spatial level at which official socio-economic census data such as population and employment statistics are systematically available from national agencies.\u00a0However, it is important to acknowledge certain limitations, such as the considerable variation in physical size and population across different\u00a0shiyakha, which can introduce scale effects typical of the Modifiable Areal Unit Problem (MAUP). To mitigate these limitations, spatial indicators were normalized, and metrics such as population and employment were calculated as densities (per square kilometer) to ensure comparability across all 413 units. The strategic relevance of GCR as a case study is further reinforced by its central role in Egypt\u2019s national development agenda. As the core of the country\u2019s economic, political, and cultural activities, the region faces accelerated pressures from rapid urbanization and increasing transportation demand. Addressing these challenges aligns directly with Egypt\u2019s Vision 2030, which prioritizes sustainable and inclusive urban development, and Cairo Vision 2050, which emphasizes the expansion of mass transit systems, the reduction of automobile dependency, and improvements in environmental quality. Consequently, Greater Cairo provides a timely and highly relevant context for evaluating TOD potential and developing evidence-based policy interventions.<\/p>\n Fig. 1 The metropolitan region of the greater Cairo. The map is produced by Arc Map v.10.5 software.<\/p>\n Methods<\/p>\n This study develops a TOD Index for the Greater Cairo Region using a spatial multi-criteria approach. Building on the framework of Evans et al.15<\/a>, and Singh et al.16<\/a> as shown in (Fig.\u00a02<\/a>), the methodology integrates spatial indicators of density, diversity, design, and accessibility into a composite index through Spatial Multi-Criteria Assessment (SMCA) in a GIS environment. The procedure involves four main steps: defining evaluation criteria and indicators, acquiring and preparing relevant spatial data, assessing indicators spatially, and integrating results to generate the TOD Index map. This index facilitates the identification of areas with varying levels of TOD potential and aids in decision-making to improve transit connectivity and inform future urban development strategies.<\/p>\n Fig. 2 Research methodology adapted from Evans IV et al.15<\/a>.<\/p>\n Identification of evaluation criteria and indicators<\/p>\n The selection of evaluation criteria and indicators followed a two-stage process. First, an initial pool of candidate indicators was compiled through a structured review of the TOD and GIS-MCDA literature, drawing on established frameworks and recent studies addressing TOD assessment in Global South and MENA contexts. This review identified indicators commonly used across the four core TOD dimensions of density, diversity, design, and accessibility, as well as context-specific variables relevant to semi-formal urban markets, such as private investment activity. Candidate indicators were retained for further consideration only if they met two criteria: (i) they had clear theoretical or empirical support in the TOD literature, and (ii) they could be measured consistently across all 413 shiyakha using available official datasets, including CAPMAS, GOPP, and OSM. Indicators that did not satisfy the second criterion, such as user-friendliness and parking availability, were excluded due to the absence of reliable shiyakha-level data. Second, the resulting shortlist of indicators was submitted to expert review to assess its relevance to the Greater Cairo context and to refine the final indicator set prior to weighting.<\/p>\n Data acquisition and preparation<\/p>\n The data necessary for creating the criteria maps were collected from multiple official and open sources. This included both spatial data in GIS formats and attribute data such as population statistics, administrative boundaries, land use patterns, and transportation networks. The datasets were obtained from the Central Agency for Public Mobilization and Statistics (CAPMAS), the General Organization for Physical Planning (GOPP), and OpenStreetMap (OSM). While most of the data were readily available, several criteria maps had to be generated manually. Table 1<\/a> presents the formats, spatial resolution, and sources of all datasets utilized in this research.\u00a0All tabular socio-economic data acquired from CAPMAS were spatially joined to the corresponding shiyakha polygons using standard GIS operations. Spatial mismatches between datasets from different years (2017 socio-economic data and 2023 infrastructure networks) were considered acceptable. Demographic distributions in established districts of Greater Cairo remain relatively stable over short periods, whereas the rapid expansion of transport infrastructure necessitated the use of the most up-to-date OSM data.\u00a0Once collected, the data were integrated with georeferenced layers and shapefiles to support subsequent analytical and computational processes.<\/p>\n Table 1 Data sources and characteristics for TOD suitability analysis.Computing indicators for potential TOD Index<\/p>\n As shown in Table 1<\/a>, nine indicators were computed to evaluate the TOD Index across the entire city region. The following subsections present a detailed explanation of the calculation methods used for each indicator.<\/p>\n Population density<\/p>\n Population density is a key driver of successful Transit-Oriented Development. By concentrating people, jobs, and services around transit hubs, cities can create sustainable, efficient, and vibrant urban environments. Balancing density with infrastructure, public amenities, and green spaces is essential to maximize the benefits of TOD.<\/p>\n Density is a fundamental factor influencing the effectiveness and success of TOD.4<\/a>,17<\/a>. According to Cervero and Guerra18<\/a>, in addition, density is a major factor in determining modal choice, with higher densities generally correlating with shorter average trip distances across all transportation modes. Population density is calculated by the number of people per square kilometer, using data from each shiyakha as provided by CAPMAS. To estimate the population within each ward, the data were proportionally distributed based on the spatial intersection of the relevant polygons using GIS tools.<\/p>\n Employment density<\/p>\n Employment density expressed as the number of jobs per unit area (jobs per square kilometer) is a key indicator in Transit-Oriented Development (TOD), as it significantly affects transit ridership, economic vitality, and overall urban sustainability19<\/a>,18<\/a>. High employment density near transit stations enhances accessibility, reduces reliance on private vehicles, and promotes economic vibrancy by clustering businesses and services within walkable distances4<\/a>. Studies suggest that TOD areas with higher employment densities contribute to increased public transit use and reduced congestion, supporting sustainable urban mobility.<\/p>\n Land use diversity<\/p>\n Land use diversity is a fundamental principle of Transit-Oriented Development (TOD), promoting a balanced mix of residential, commercial, office, and recreational spaces within proximity to transit stations19<\/a>,4<\/a>. A well-integrated land use mix enhances accessibility, reduces travel distances, and supports active transportation modes such as walking and cycling. Higher land use diversity in TODs is associated with increased transit ridership, lower vehicle dependency, and improved urban vitality18<\/a>,20<\/a>. Several measures, including the entropy index, are employed to quantify land use diversity by evaluating the degree of balance among various land use types within a specific area21<\/a>. Effective land use integration in TODs fosters compact, sustainable, and economically resilient urban environments while reducing urban sprawl and congestion.<\/p>\n In this study, land use diversity was measured using an entropy-based index applied to the proportional composition of land-use categories within each shiyakha, following the formulation proposed by Ritsema van Eck and Koomen21<\/a>. The standard entropy formulation was adapted to reflect the relative shares of land-use types within each analysis unit rather than simple land-use counts, thereby capturing internal spatial diversity more accurately at the neighborhood scale21<\/a>. This adaptation is particularly suitable for Greater Cairo, where densely built, mixed, and historically developed urban fabrics exhibit substantial local variation in land-use composition that would not be adequately represented by coarse administrative averages. Although the entropy index does not capture qualitative differences in land use intensity or function, it provides a robust proxy for spatial heterogeneity, land use balance, and diversity within complex urban environments21<\/a>. The entropy value for each shiyakha was computed using Eqs. (1<\/a>) and (2<\/a>), as shown below.<\/p>\n $$\\text{L}\\text{U}\\text{d} (\\text{i}) =\\frac{-\\sum Qlui \\,x \\,Ln(Qlui)}{Ln(n)}$$<\/p>\n \n (1)\n <\/p>\n $$Qlui=\\frac{Slui}{Si}$$<\/p>\n \n (2)\n <\/p>\n Lui represents the land use class (1, 2\u2026, n) within the analysis window i,<\/p>\n Qlu denotes the proportion of a specific land use within that window,<\/p>\n Slui is the total area of the specific land use in the analysis window; and Si refers to the total area of the analysis window itself. The formula is applicable only to areas containing more than one land use category otherwise, it yields a value of zero, indicating the absence of land use diversity.<\/p>\n Mixed-ness of land use<\/p>\n This indicator aims to evaluate the degree of walkability and cyclability within the study area. Unlike the land use diversity index, which captures the overall mix of land use types, this measure focuses specifically on the level of integration between residential and non-residential land uses. Previous studies, including those by Evans IV et al.15<\/a>, Marshall and Garrick22<\/a>, have demonstrated that a well-integrated combination of residential and other land uses improves accessibility for non-work-related trips undertaken by walking or cycling.<\/p>\n To measure this degree of integration, the interaction between residential and other land uses was quantified using a modified version of the formula proposed by Marshall and Garrick22<\/a>. The adapted equation used in this study is presented in Eq.\u00a0(3<\/a>).<\/p>\n $$\\text{M}\\text{I} =\\frac{\\sum Sc}{\\sum (Sc+Sr)}$$<\/p>\n \n (3)\n <\/p>\n where, MI denotes the Mixed-ness Index; Sc represents the total combined area of commercial, industrial, and institutional land uses; and Sr indicates the total area of residential land use within the analysis neighborhood i.<\/p>\n Intersection density<\/p>\n Intersections play a vital role in promoting walking and cycling by shortening travel distances and improving route efficiency. A higher intersection density is typically linked to greater walkability4<\/a>,15<\/a> and, consequently, enhanced cyclability, as it offers pedestrians multiple route choices and the potential to minimize overall travel distances23<\/a>. To quantify intersection density, the number of intersections per square kilometer within the study area was calculated. This analysis was conducted using road network data, processed in ArcGIS to derive the corresponding indicator.<\/p>\n Roads network<\/p>\n Road density, defined as the total length of roads per unit area, is a key indicator in Transit-Oriented Development (TOD) as it influences connectivity, accessibility, and multimodal transportation efficiency4<\/a>,23<\/a>. A well-connected and dense road network enhances walkability and cyclability by providing multiple route options, reducing travel distances, and improving access to transit stations22<\/a>. Higher road density is generally associated with improved pedestrian and cyclist safety, as slower vehicular speeds and increased intersections contribute to safer environments24<\/a>,25<\/a>. The integration of a fine-grained road network in TOD areas facilitates efficient transit operations, minimizes congestion, and supports sustainable urban development18<\/a>,19<\/a>. Geographic Information Systems (GIS) are commonly used to measure road density and analyze its impact on TOD effectiveness.<\/p>\n Economic development<\/p>\n Economic development is a fundamental objective of Transit-Oriented Development (TOD), as well-planned transit investments stimulate business activity, attract investment, and enhance property values19<\/a>,26<\/a>. TOD fosters job creation by clustering commercial, retail, and office spaces near transit hubs, facilitating increased employment opportunities and business productivity18<\/a>,27<\/a>. Moreover, improved accessibility in TOD areas reduces transportation costs, increases consumer spending, and enhances the competitiveness of urban centers3<\/a>.<\/p>\n Studies indicate that mixed-use development in TODs contributes to higher land values and rental performance, further supporting economic growth28<\/a>.<\/p>\n The economic impact of TOD is often assessed through property value analysis, employment density metrics, and business activity indicators, providing insights into the long-term benefits of transit-oriented planning29<\/a>. High levels of economic development are closely associated with stronger TOD performance. The presence of various establishments and private investment opportunities within an area serves as a key indicator of economic activity. These factors represent essential performance measures and were quantified using data obtained from CAPMAS.<\/p>\n Standardization (Normalization)<\/p>\n Given the heterogeneity of the selected indicators measured in disparate units such as persons per sq. km, percentages, and kilometers per square kilometer standardization process was essential to ensure comparability. The Min\u2013Max normalization method was applied to rescale all nine indicator values to a unified range of (0\u20131), where 0 represents the minimum performance and 1 represents the maximum performance observed across the 413 shyiakhas. This linear transformation preserves the relative distribution of the data while eliminating unit bias16<\/a>.<\/p>\n Computing potential TOD Index using SMCA<\/p>\n Spatial Multi-Criteria Analysis (SMCA) was employed to aggregate the nine standardized indicators into a composite TOD Index, following established GIS-based TOD methodologies16<\/a>.<\/p>\n The expert weighting exercise employed a purposive sampling strategy. Experts were eligible for inclusion if they held a specialization in urban planning or transport planning and had a minimum of 20\u00a0years of professional or academic experience in their field. Based on these criteria, 20 experts affiliated with leading academic and planning institutions in Egypt, including Cairo University, Ain Shams University, Al-Azhar University, and the General Organization for Physical Planning (GOPP), were invited to participate. Of these, 15 experts completed the questionnaire, corresponding to a response rate of 75%. Participants were asked to rate each criterion and indicator on a five-point Likert scale (1\u2009=\u2009least important, 5\u2009=\u2009most important) according to its relative importance for assessing TOD potential in the Greater Cairo context. The full questionnaire and participating experts’ affiliations are provided in Supplementary Table S1.<\/p>\n Weights were derived using a hybrid ranking approach combining the modified Borda count and the Rank Sum method. Expert ratings were first aggregated to generate a Borda score for each criterion and indicator, then normalized and used to establish the ranking order. The ranked criteria were subsequently converted into normalized weights using the Rank Sum method, ensuring that higher-ranked criteria received proportionally greater weights. This procedure provided a transparent and reproducible weighting structure suitable for SMCA-based TOD assessment. In this approach, the Borda score (Sc) for each criterion was calculated by aggregating the frequency of expert ratings, as expressed in Eq.\u00a0(4<\/a>):<\/p>\n $$Sc=\\sum_{n=1}^{N}(\\text{K}\\text{n}\\text{i}. \\text{n})$$<\/p>\n \n (4)\n <\/p>\n where Kni denotes the number of stakeholders who assigned a score of n (on a Likert scale from 1 to 5) to criterion i.<\/p>\n To ensure comparability, the Borda scores were normalized by dividing by the maximum possible score (5\u2009\u00d7\u200915\u2009=\u200975). The resulting Borda scores indicated that land use diversity and residential density were the highest-ranked indicators (see Supplementary Table S2). Based on these scores, criteria were ranked and subsequently transformed into normalized weights using the Rank Sum method. This method ensures that higher-ranked criteria receive proportionally greater weights, as shown in Supplementary Table S3. The weight W(k) for the k-th ranked element among n alternatives is calculated using Eq.16<\/a>:<\/p>\n $$W(k)=\\frac{n+1-k}{\\sum_{i=1}^{n}(\\text{n}+1-\\text{i})}$$<\/p>\n \n (5)\n <\/p>\n where W(k) represents the normalized weight assigned to the criterion ranked k, n denotes the total number of criteria, and i serves as the index for summation, taking values from 1 to n.<\/p>\n This two-step procedure provided a transparent and reproducible weighting structure suitable for SMCA-based TOD assessment. The composite TOD Index for each shiyakha was then computed using the Weighted Linear Combination (WLC) method, as described in Eq.\u00a0(6<\/a>):<\/p>\n $$TOD\\, Index=\\sum_{n=1}^{N}(\\text{W}\\text{i}*\\text{R}\\text{i})$$<\/p>\n \n (6)\n <\/p>\n where Wi is the weight assigned to indicator i, Ri is the standardized value of indicator i, and n is the total number of indicators. The final normalized weights used in the TOD Index aggregation are presented in Table 2<\/a> in the Results section.<\/p>\n Table 2 Criterion weights, ranking order, and the contribution to the criterion\u2019s weight.Model validation overview<\/p>\n To ensure the robustness and reliability of the developed TOD Index, a validation procedure was incorporated into the methodological framework. The process involved both spatial and statistical verification steps to compare the model outputs with real-world urban and transport characteristics across the Greater Cairo Region.<\/p>\n The spatial validation was based on identifying the level of agreement between areas with high TOD Index values and actual zones characterized by dense mixed-use patterns and proximity to metro lines. The statistical validation used correlation analysis to confirm the relationship between TOD Index values and observed population and employment densities.<\/p>\n![\"Fig.](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2026\/10\/41598_2026_71012_Fig1_HTML.png\")
<\/p>\n![\"Fig.](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2026\/10\/41598_2026_71012_Fig2_HTML.png\")
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